kurtosis

Yep, and for some reason unknown to me, the Excel function for kurtosis actually calculates excess kurtosis.

In most maths packages log is the natural log and kurtosis is the by-definition kurtosis.

An it is statistics 101 that if you change the skewness and kurtosis of a distribution if effects not just those at the top.

More interestingly by using mean, variance (or sd), skew, and kurtosis, you can describe almost any centrally concentrated distribution.

For example, if Y is the sum of n independent and identically distributed random variables with the distribution of X, then the kurtosis of Y is 1/n times the kurtosis of X.

It is the kurtosis-maximization of ICA that tends to produce filters mimicking those found in (early layers of) visual cortex.

Incidentally, why didn't you extend the kurtosis scale to allow for a platykurtic distribution?

Skewness (third cumulant) decays at 1/√n, excess kurtosis at 1/n, and so on up.

As kurtosis here mentions, you often learn real practical skills while doing research.

Typically, they can histogram # of connections and use central moments such as kurtosis to determine if a particular node has too many connections.

Sliding the kurtosis indicator changes the left distribution, which makes sense.

Negative excess kurtosis indicates a platykurtic distribution, which doesn’t necessarily have a flat top but produces fewer or less extreme outliers than the normal distribution.

I think you mean to say something like "kurtosis" - which is, if anything, obscured to the eye by the process of flattening by scaling.

@kurtosis: You should rephrase your question as, "why haven't cells evolved to be more heat insensitive".

That says exactly the same thing I just said: > Excess kurtosis, typically compared to a value of 0, characterizes the “tailedness” of a distribution.

And it's hard to look at the evidence and conclude he's wrong here; it's simply objective fact that many of the interesting distributions are in fact prone to "black swans", there's a statistical test that shows it (kurtosis) quite clearly.

Kurtosis[0] is a term I came across when dealing with random vibration analysis, but I understand very little if it.